Technical Field
Embodiments of the subject matter disclosed herein generally relate to methods and systems for characterizing a subsurface and, more particularly, to mechanisms and techniques for processing data related to the subsurface for generating an ensemble of models that describe the subsurface, allow flow predictions to be made and/or generate uncertainty estimation.
Discussion of the Background
A forward model (e.g., reservoir simulation, geo-mechanical model) is a tool that gives insight into dynamic rock and fluid properties for evaluation of past reservoir performance, prediction of future reservoir performance, and reserve estimation. Reservoir simulation might be the only reliable method to predict performance of large complex reservoirs. Additionally, simulation results, when combined with 3-D time-lapse seismic results, form a reservoir-monitoring tool for mapping changes associated with production that can be used to understand past reservoir behavior for future prediction.
In standard geo-modeling workflows, integration of static and dynamic data is usually performed sequentially, as follows: (i) petrophysical geo-models are initially built using log data and geophysical data and (ii) the models are then perturbed to match historic production data (e.g., flow, saturation and other parameters that are measured at wells associated with the subsurface describing the reservoir). For this last step, ensemble-based methods of optimization [1-3] have become popular due to their flexibility, computational efficiency, and ability to match dynamic data using a wide range of parameters and quantify posterior uncertainties. A quadratic approximation of the objective function is statistically estimated from an ensemble of realizations and is used to update each individual member. However, for high dimensional problems, the size of the ensemble is limited as a full fluid flow simulation needs to be run for each member. For this reason, the resulting approximation may be noisy and leads to spurious updates which will damage important geological features of the prior models and significantly reduce the variability of the ensemble.
Thus, there is a need to develop a method capable of matching the production history of the reservoir, preserving the original models and also being able to accurately predict the future behavior of the reservoir.